[Rtk-users] ADMMTVReconstruction

[Cyril Mory]

Hello Howard,

Good to hear that you're using RTK :)
I'll try to answer all your questions, and give you some advice:
- In general, you can expect some improvement over rtkfdk, but not a 
huge one
- You can find the calculations in my PhD thesis 
https://tel.archives-ouvertes.fr/tel-00985728 (in English. Only the 
introduction is in French)
- Adjusting the parameters is, in itself, a research topic (sorry !). 
Alpha controls the amount of regularization and only that (the higher, 
the more regularization). Beta, theoretically, should only change the 
convergence speed, provided you do an infinite number of iterations (I 
know it doesn't help, sorry again !). In practice, beta is ubiquitous 
and appears everywhere in the calculations, therefore it is hard to 
predict what effect an increase/decrease of beta will give on the 
images. I would keep it as is, and play on alpha
- 3 iterations is way too little. I typically used 30 iterations. Using 
the CUDA forward and back projectors helped a lot maintain the 
computation time manageable
- The quality of the results depends a lot on the nature of the image 
you are trying to reconstruct. In a nutshell, the algorithm assumes that 
the image you are reconstructing has a certain form of regularity, and 
discards the potential solutions that do not have it. This assumption 
partly compensates for the lack of data. ADMM TV assumes that the image 
you are reconstructing is piecewise constant, i.e. has large uniform 
areas separated by sharp borders. If your image is a phantom, it should 
give good results. If it is a real patient, you should probably change 
to another algorithm that assumes another form of regularity in the 
images (try rtkadmmwavelets)
- You can find out whether you typical images can benefit from TV 
regularization by reconstructing from all projections with rtkfdk, then 
applying rtktotalvariationdenoising on the reconstructed volume (try 50 
iterations and adjust the gamma parameter: high gamma means high 
regularization). If this denoising implies an unacceptable loss of 
quality, stay away from TV for these images, and try wavelets

I hope this helps

Looking forward to reading you again,
Cyril

On 12/12/2014 06:42 PM, Howard wrote:
> I am testing the ADMM total variation reconstruction with sparse data 
> sample. I could reconstruct but the results were not as good as 
> expected. In other words, it didn't show much improvement compared to 
> fdk reconstruction using the same sparse projection data.
> The parameters I used in ADMMTV were the following:
> --spacing 2,2,2 --dimension 250,100,250 --alpha 1 --beta 1000 -n 3
> while the fdk reconstruction parameters are:
> --spacing 2,2,2 --dimension 250,100,250 --pad 0.1 --hann 0.5
> The dimensions were chosen to include the entire anatomy. 72 
> projections were selected out of 646 projections for a 360 degree scan 
> for both calculations.
> What parameters and how can I adjust (like alpha, beta, or 
> iterations?) to improve the ADMMTV reconstruction? There is not much 
> description of this application from the wiki page.
> Thanks,
> -howard
>
>
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-- 
--
Cyril Mory, Post-doc
CREATIS
Leon Berard cancer treatment center
28 rue Laënnec
69373 Lyon cedex 08 FRANCE

Mobile: +33 6 69 46 73 79

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